A User Behavior Perception Model Based on Markov Process

Authors:
Zhan Shi;Yuhong Li;Li Han;Jian Ma
Affiliations:
State Key Laboratory of Networking and Switching Technology, BUPT, Beijing, P.R. China 100876;State Key Laboratory of Networking and Switching Technology, BUPT, Beijing, P.R. China 100876;Nokia Research Center Beijing, Beijing, P.R. China 100176;Nokia Research Center Beijing, Beijing, P.R. China 100176
Venue:
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Year:
2009

Citing 6
Cited 1

A Survey of Context-Aware Mobile Computing Research

A Survey of Context-Aware Mobile Computing Research
Probabilistic User Behavior Models

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A framework of combining Markov model with association rules for predicting web page accesses

AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Hidden Markov Models for Activity Recognition in Ambient Intelligence Environments

ENC '07 Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science
Context-Aware Computing Applications

WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Self-Learning Disk Scheduling

IEEE Transactions on Knowledge and Data Engineering

Towards group behavioral reason mining

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nowadays user behavior perception becomes more and more important in scientific experiments and people's daily lives, especially in mobile services. This paper presents a model which can be used to perceive user's behavior on intelligent mobile terminals based on Markov process. The model combines the syntax model and the probability transition model, and introduces also other parameters like weight. It can perceive users behavior through different kinds of contexts, such as users' reading history. According to the perceived behavior, the model can better perceive the user's behavior, and therefore allows better mobiles services experiences to users. A system architecture based on the presented model has been introduced and implemented, and a verification method of the model has also been given.